Totally agree with you on why an academic masters program is more rigorous. People can get caught up on “why aren’t we focusing on real world applications?” when nailing down the theory will pay dividends in the future.
🎯 Key Takeaways for quick navigation: 00:03 *🎓 Differentiating Academic and Professional Degrees* - Academic masters focus on theory, rigorous training in math, stats, and computer science, often lacking hands-on application. - Professional degrees, like MBAs, emphasize vocational training for specific job tasks, with more hands-on, industry-focused learning. - Academic programs typically require 3-4 courses per semester, ensuring depth of study, while professional degrees may allow for more courses with less rigor. 07:34 *📚 Importance of Academic Rigor in Quantitative Finance* - Academic masters provide a deeper understanding of quantitative finance principles, essential for excelling in roles like derivatives pricing or risk management. - Programs lacking academic rigor often result in graduates needing extensive on-the-job training, impacting their competitiveness in the job market. - Academic rigor ensures graduates are well-prepared with necessary academic tools for success in quantitative finance careers. 11:18 *💼 Quantitative Finance Industry Expectations and Challenges* - The industry demands rigorous education, with academic degrees preferred over professional ones due to the depth of knowledge they provide. - Programs not meeting industry standards in academic rigor may struggle to adequately prepare graduates for quantitative finance roles. - Competition for quantitative finance positions is intense, necessitating a strong academic foundation for job readiness and career advancement. 19:10 *📚 The importance of a rigorous quant finance thesis* - The quant finance thesis should involve legitimate, rigorous research, not superficial factor models. - Instructors should include both academic professors and industry practitioners to provide cutting-edge insights and real-world problems. - Students should engage in hands-on research, addressing actual industry challenges, under the mentorship of faculty. 21:04 *🎓 Summary: Academic rigor, course load, and professional mentorship* - A balance of academic rigor and industry relevance is crucial for a comprehensive quantitative finance master's program. - The proposed curriculum includes 12 courses spread over two years, emphasizing the depth of learning. - Professional mentorship and career services are essential for grooming students to succeed in the industry, including practical skills like resume writing and interviewing techniques. Made with HARPA AI
Hi Dimitri! What are your thoughts on Quant Masters from top UK unis where the majority of them are 1 year courses? From your experiences, do you notice a difference between UK and US quants in their academically rigorous? Thank you!
Back in the 90's, I was looking to leave a career in quantitative equity trading to get a PhD in Finance. One of the quants who used to work with John Meriwether at Solomon referred me to finance professors at the top business schools in SF and NY. I applied to the PhD programs and took a week off work to visit them. But I was disappointed to find the professors completely disconnected from real-world problems. Their focus seemed to be getting published in journals which no one outside academia bothered to read. I joined a hedge fund instead.
I completely agree that 2 years should be ideal for Masters. Even 2 years of study isn't enough. In order to become successful you need to be able to live and breathe in Quantitative Finance. From wherever you study, one should have an individual spark to become successful and bear challenges.
I don’t know if the academic vs professional is spot on. Baruch’s MFE is a Master in Financial Engineering. Dan Stefanica told me personally when I was on campus last year that it is a professional program, not an academic program. They have the best placement that I have seen. I’m in a Master in Statistics program right now that is very highly ranked and so far, very rigorous. 3 courses in my first semester required an extraordinary amount of work. 3-4 semesters in total plus a statistical consulting practicum where we do consulting for large corporations in the summer instead of an internship (I think this is common in Stats programs from what I have seen). But I understand your concern about MBA-like programs where it’s almost an executive overview of QF with no rigor. The rest of the video is great. I always appreciate your perspectives. Question: What topics should MS Stats students self teach if they want to work in the Quant Finance world? Stochastic Processes, Time Series, Bayesian Methods? Any books that you recommend for the transition into QF?
This was one of your best videos in my opinion. Also, I'm not sure if you've changed anything but I only got 1 ad instead of like 6 so thank you for that its greatly appreciated.
Dimitri your video has been absolutely helpful for me. Can you make a video on debunking some myths about technical analysis? I have heard that most of the indicators used in TA has been developed for econometrics data considering stationarity and day traders randomly apply them to charts.
Very well said. Nice video and explanations, Dimitri! You have motivated and helped me a lot to keeping my studies and goals in quantitative finance focused on the main points that really matter. You are my inspiration. Thank you for dedicate your time to share this liquid gold contents and experience with us.
Hi Dimitri, I would be very interested on your view about the CQF certificate from Paul Willmot. I have already a MSc in Quantitative Finance, but as you mentioned I don't think a one year degree is sufficient to cover in depth this field.
This is probably the most asked question and I had a video on it but never posted it. There are some political reasons on why I never released it but here is my quick take. The CQF was designed to be a certification for those who had already been in the industry a long time and needed to sharpen their quant skills as the industry had changed a lot. For this purpose I think it is a great choice. Now as a stand alone degree...no it is not comparable to a good masters degree. As a designation/certificate it could also be great however I think it is over priced.
Dear Dimitri thank you very much for this fascinating video, indeed this video is very helpful for me. Particularly, these days that I need to choose one of two programmes; a) Master's in quantitative finance at the University of Kiel (Germany) and b) Master's in Quantitative Finance at Rotterdam School of Economics. I would be very happy and grateful to you if you are able to advice the career opportunity for Quant Finance graduates in European market. Once again many thanks, please keep producing such educated content.
Hi Dimitri! I’m currently finishing my undergraduate in Statistics. There is one thing that confused me in another video of yours, i have to choose beetween a master in statistics or in financial engineering, from your videos i understood that your general advice would be to continue with the “more math rigorous” statistic career. Untill i saw your video about the best degrees for quant finance where you said that you would suggest financial engineering master over something like stats because it’s more math focused🤔. I don’t get it, did i misunderstood or miss something? How can financial engineering be more math focused than statistics? Please if everyone understood better than me what he meant let me know, thanks!!!😁🙏🙏
@danielfilippi5201 there is no best degree. They both have strengths and weaknesses. Financial engineering is more applied to the job market which is good however many programs will lack great depth in mathematics. Statistics degree are good because you learn more modeling however they also lack a lot of math and they are not industry specific. A math degree is great but it often lacks statistics and industry application. There really is no perfect degree and it depends on your undergrad education. You need to cover math, stats, and CS but the mix will depend on the job you want. If you have a statistics undergrad, I would try and get a quant masters like financial engineering or Computational Finance as you will be missing the finance and application of math and stats to finance.
Hello Dimitri! I want to break into the finance industry. What do you think about a totally academic master's? Some people say the degree that is so academic (traditional statistics masters, etc.) would not be suitable to get a job. Moreover, even they have a rigorous curriculum, they are less preferred than Ph.D.s. Is academic degree disadvantageous than professional degrees in terms of getting a job, and disadvantageous than Ph.D. degrees in terms of research ability and rigorousness?
Even though a MSc in Quantitative Finance or Financial Mathematics are professional degrees, can it still be better than an academic degree like Mathematics, as it is specific to what you will use on the job rather than many more broader topics that may not have quant finance applications?
Hi Dimitri. It sounds like if you want to be a good quant you should basically do an undergrad in CS or EE, then a MS in applied econ and then a MS in CS.
amazing video. very instructive. very well structured curriculum. I hoped that you would mention a bit more about the numerical method part. which courses would be appropriate? nonlinear optimization, discrete optimization/ numerical pde etc? i'm starting my master in maths here in germany, we can structure our own courses. im finishing my bachelor in mathematics-specialized in optimization and statistics, thesis on deep learning- with minor cs. Im considering to combine statistics, optimization and cs-machine learning in my master, but not really sure if i should reduce optimization a bit and take some more cs- data science. we can choose two fields of specialization, and my options are: A- stats and cs- machine learning and data science and then a bit of opti as a side dish. B- stats and optimization, and a bit of cs- machine learning as a minor. what would you comment on this?
Great list Dimitri. If someone is not necessarily interested in Quant Finance as a career, but simply interested as a hobby, and thus the academic rigor and time investment is neither desired nor affordable, what in your opinion would be a good source to learn the basics of all these topics?
The rigor piece is very important even when studying quant finance as a hobby. The small details is what makes a model perform well or fail quickly. If you are wanting to self study, textbooks are your best path. You DON'T need a ton of books either. Take your time and just work through a few basic books and then add another book in areas that you realize you need to know more about. I will also be creating a Udemy course that is an intro to quant finance. I will expect students to have no quantitative background. If the course does well, I will consider building more of them. The plan is to start very basic and then slowly build to more complex ideas without leaving people behind.
@@DimitriBianco That sounds fantastic. I've been reading some of the articles on QuantStart and other basic quant resources, and it's some really interesting material. I have a background in engineering so the foundations in math and CS aren't really stumbling blocks, and there is no problem with rigor (other than the time + energy commitment required to get full coverage of the field). I, as many others, prefer self-study and have found great success in that. As you've mentioned on your videos many times, however, there is a lot of hype and ego in this field, an overwhelming amount of 'gambling addict' mentality, and it's somewhat difficult to sort between that and the legitimate art unless you either have the hands-on experience or guidance from an expert on what type of material is relevant.
I think using years as a metric for masters is not good. I would rather you say units. Some people can take lax schedule for a years worth of units in masters and make it two years.
Statistics degrees are great! I've mentioned in a few other videos that companies do hire quite a few stats masters and phds. As for applied vs traditional...it really depends on the program and how they define applied vs traditional.
@@DimitriBianco from what I’ve researched applied is less theory than a traditional statistics masters. Applied has more of a programming component but less theory, almost like “data science” but with more of a quantitative focus. Traditional stat is almost no programming, more deeper in theory. If I were to compare two people one with applied stats ms and one with pure stats ms, the applied stats is a better programmer but the pure stats is better at proofs/theory.
Dimitri, I'm having a very hard time picking a graduate school to go to. You recommended Risk Management as a career for me because I like math, stats, and finance but don't want to do software engineering, and you recommended I pursue a graduate degree in statistics. However, there are so many schools offering statistics degrees without seeming to offer financial engineering or financial mathematics courses. Filtering is hard when there are thousands of programs. How can I figure out the best program to pursue? Also, you mentioned that many would discount an unheard-of school. Does this mean I would want to go to a school that's well known specifically for statistics, a school that is well known specifically for financial engineering/mathematics, or a school that is well known in general? I feel so lost and anxious...
To my knowledge no program exists. This information should be used to help select a program though. For example 2 year programs are very rare. However a 1.5 year program would be better than a 1 year program.
I can't speak directly for NYU but math and stats are good undergraduate degrees for quant finance masters preparation. I have a list of programs however it is not exhaustive (I have limited resources to review programs). www.fancyquantnation.com/quant-finance-list
hey dimitri love your videos. what paths are there into Quant having a UK bachelors degree in computer science from a top 25 overall school in the UK and having significant experience in coding projects and also lots of experience with machine learning and NLP projects. I'm 20 and i also have previous work experience at a startup for 1 year doing Data Science / Analytics
What is your opinion on PRM? Is it any good like FRM? I have a bsc in economics and studied msc in applied statistics and I am considering to take exams for either PRM or FRM, because I want to start my career as a credit risk modeler, or even market risk modeler, but preferably credit risk.
Hey thanks for the video. Do you think it's wise to go straight into a Master's after graduating, or should I work for a few years then do a master's? Cheers
Yes, if your degree is from outside the US it will be more challenging to find work in the US. For quants I've only worked with two who had graduate degrees not from the US. They both had degrees from Canada though which is close.
I might have had one colleague with a degree from eastern Europe but they were also much older. It's been too many years for me to remember the details.
This is a great video! Long overdue. Great recommendations, FE or mathematical finance masters programs should take note. 👍🏼
Totally agree with you on why an academic masters program is more rigorous. People can get caught up on “why aren’t we focusing on real world applications?” when nailing down the theory will pay dividends in the future.
🎯 Key Takeaways for quick navigation:
00:03 *🎓 Differentiating Academic and Professional Degrees*
- Academic masters focus on theory, rigorous training in math, stats, and computer science, often lacking hands-on application.
- Professional degrees, like MBAs, emphasize vocational training for specific job tasks, with more hands-on, industry-focused learning.
- Academic programs typically require 3-4 courses per semester, ensuring depth of study, while professional degrees may allow for more courses with less rigor.
07:34 *📚 Importance of Academic Rigor in Quantitative Finance*
- Academic masters provide a deeper understanding of quantitative finance principles, essential for excelling in roles like derivatives pricing or risk management.
- Programs lacking academic rigor often result in graduates needing extensive on-the-job training, impacting their competitiveness in the job market.
- Academic rigor ensures graduates are well-prepared with necessary academic tools for success in quantitative finance careers.
11:18 *💼 Quantitative Finance Industry Expectations and Challenges*
- The industry demands rigorous education, with academic degrees preferred over professional ones due to the depth of knowledge they provide.
- Programs not meeting industry standards in academic rigor may struggle to adequately prepare graduates for quantitative finance roles.
- Competition for quantitative finance positions is intense, necessitating a strong academic foundation for job readiness and career advancement.
19:10 *📚 The importance of a rigorous quant finance thesis*
- The quant finance thesis should involve legitimate, rigorous research, not superficial factor models.
- Instructors should include both academic professors and industry practitioners to provide cutting-edge insights and real-world problems.
- Students should engage in hands-on research, addressing actual industry challenges, under the mentorship of faculty.
21:04 *🎓 Summary: Academic rigor, course load, and professional mentorship*
- A balance of academic rigor and industry relevance is crucial for a comprehensive quantitative finance master's program.
- The proposed curriculum includes 12 courses spread over two years, emphasizing the depth of learning.
- Professional mentorship and career services are essential for grooming students to succeed in the industry, including practical skills like resume writing and interviewing techniques.
Made with HARPA AI
man you posted this at the perfect time as I am comparing programs. Thank you!
Hi Dimitri! What are your thoughts on Quant Masters from top UK unis where the majority of them are 1 year courses? From your experiences, do you notice a difference between UK and US quants in their academically rigorous? Thank you!
Back in the 90's, I was looking to leave a career in quantitative equity trading to get a PhD in Finance. One of the quants who used to work with John Meriwether at Solomon referred me to finance professors at the top business schools in SF and NY. I applied to the PhD programs and took a week off work to visit them. But I was disappointed to find the professors completely disconnected from real-world problems. Their focus seemed to be getting published in journals which no one outside academia bothered to read. I joined a hedge fund instead.
What would you recommend to someone wanting to work for a hedge fund? Thx
I completely agree that 2 years should be ideal for Masters. Even 2 years of study isn't enough. In order to become successful you need to be able to live and breathe in Quantitative Finance.
From wherever you study, one should have an individual spark to become successful and bear challenges.
Yeah two years not enough strongly agree
And once you hit the industry there is never enough time to study. I really miss my days as a student as my life was so focused around learning.
I don’t know if the academic vs professional is spot on. Baruch’s MFE is a Master in Financial Engineering. Dan Stefanica told me personally when I was on campus last year that it is a professional program, not an academic program. They have the best placement that I have seen.
I’m in a Master in Statistics program right now that is very highly ranked and so far, very rigorous. 3 courses in my first semester required an extraordinary amount of work. 3-4 semesters in total plus a statistical consulting practicum where we do consulting for large corporations in the summer instead of an internship (I think this is common in Stats programs from what I have seen).
But I understand your concern about MBA-like programs where it’s almost an executive overview of QF with no rigor.
The rest of the video is great. I always appreciate your perspectives.
Question: What topics should MS Stats students self teach if they want to work in the Quant Finance world? Stochastic Processes, Time Series, Bayesian Methods? Any books that you recommend for the transition into QF?
Please study Principles of Financial Engineering by Robert kowoski
Good breakdown, that should help students in undergrad like myself to prepare them well for grad school.
This was one of your best videos in my opinion. Also, I'm not sure if you've changed anything but I only got 1 ad instead of like 6 so thank you for that its greatly appreciated.
Thanks for the feedback. I haven't made any changes in the ads. Maybe UA-cam is updating their algorithm.
Two MBAs already watched the video XD
@Bain Number One he counted the dislikes. A 3rd MBA has also watched the video.
@Bain Number One He kind of dismissed the academic rigor of MBA programs in the video
Now 4 mba
Dimitri your video has been absolutely helpful for me.
Can you make a video on debunking some myths about technical analysis? I have heard that most of the indicators used in TA has been developed for econometrics data considering stationarity and day traders randomly apply them to charts.
Very well said. Nice video and explanations, Dimitri! You have motivated and helped me a lot to keeping my studies and goals in quantitative finance focused on the main points that really matter. You are my inspiration. Thank you for dedicate your time to share this liquid gold contents and experience with us.
Thanks for the feedback and being a part of the channel!
Hi Dimitri, I would be very interested on your view about the CQF certificate from Paul Willmot. I have already a MSc in Quantitative Finance, but as you mentioned I don't think a one year degree is sufficient to cover in depth this field.
This is probably the most asked question and I had a video on it but never posted it. There are some political reasons on why I never released it but here is my quick take. The CQF was designed to be a certification for those who had already been in the industry a long time and needed to sharpen their quant skills as the industry had changed a lot. For this purpose I think it is a great choice. Now as a stand alone degree...no it is not comparable to a good masters degree. As a designation/certificate it could also be great however I think it is over priced.
@@DimitriBianco Thank you for sharing your thoughts!
Dear Dimitri thank you very much for this fascinating video, indeed this video is very helpful for me. Particularly, these days that I need to choose one of two programmes; a) Master's in quantitative finance at the University of Kiel (Germany) and b) Master's in Quantitative Finance at Rotterdam School of Economics.
I would be very happy and grateful to you if you are able to advice the career opportunity for Quant Finance graduates in European market.
Once again many thanks, please keep producing such educated content.
I wish I had more insight into Europe's quant market. I have a few contacts over there but not enough to give Europe specific advice.
I more or less have the same courses (I have more courses overall) in a Msc in Quantitative Economics
Where are u planning to do this masters from
Hi Dimitri! I’m currently finishing my undergraduate in Statistics.
There is one thing that confused me in another video of yours, i have to choose beetween a master in statistics or in financial engineering, from your videos i understood that your general advice would be to continue with the “more math rigorous” statistic career. Untill i saw your video about the best degrees for quant finance where you said that you would suggest financial engineering master over something like stats because it’s more math focused🤔.
I don’t get it, did i misunderstood or miss something? How can financial engineering be more math focused than statistics? Please if everyone understood better than me what he meant let me know, thanks!!!😁🙏🙏
@danielfilippi5201 there is no best degree. They both have strengths and weaknesses. Financial engineering is more applied to the job market which is good however many programs will lack great depth in mathematics. Statistics degree are good because you learn more modeling however they also lack a lot of math and they are not industry specific. A math degree is great but it often lacks statistics and industry application.
There really is no perfect degree and it depends on your undergrad education. You need to cover math, stats, and CS but the mix will depend on the job you want. If you have a statistics undergrad, I would try and get a quant masters like financial engineering or Computational Finance as you will be missing the finance and application of math and stats to finance.
Hello Dimitri! I want to break into the finance industry. What do you think about a totally academic master's? Some people say the degree that is so academic (traditional statistics masters, etc.) would not be suitable to get a job. Moreover, even they have a rigorous curriculum, they are less preferred than Ph.D.s. Is academic degree disadvantageous than professional degrees in terms of getting a job, and disadvantageous than Ph.D. degrees in terms of research ability and rigorousness?
I'll answer this in a video as I'm sure others will want to know.
Even though a MSc in Quantitative Finance or Financial Mathematics are professional degrees, can it still be better than an academic degree like Mathematics, as it is specific to what you will use on the job rather than many more broader topics that may not have quant finance applications?
Hi Dimitri. It sounds like if you want to be a good quant you should basically do an undergrad in CS or EE, then a MS in applied econ and then a MS in CS.
amazing video. very instructive. very well structured curriculum.
I hoped that you would mention a bit more about the numerical method part.
which courses would be appropriate?
nonlinear optimization, discrete optimization/ numerical pde etc?
i'm starting my master in maths here in germany, we can structure our own courses.
im finishing my bachelor in mathematics-specialized in optimization and statistics, thesis on deep learning- with minor cs.
Im considering to combine statistics, optimization and cs-machine learning in my master,
but not really sure if i should reduce optimization a bit and take some more cs- data science.
we can choose two fields of specialization, and my options are:
A- stats and cs- machine learning and data science and then a bit of opti as a side dish.
B- stats and optimization, and a bit of cs- machine learning as a minor.
what would you comment on this?
Great list Dimitri. If someone is not necessarily interested in Quant Finance as a career, but simply interested as a hobby, and thus the academic rigor and time investment is neither desired nor affordable, what in your opinion would be a good source to learn the basics of all these topics?
The rigor piece is very important even when studying quant finance as a hobby. The small details is what makes a model perform well or fail quickly.
If you are wanting to self study, textbooks are your best path. You DON'T need a ton of books either. Take your time and just work through a few basic books and then add another book in areas that you realize you need to know more about. I will also be creating a Udemy course that is an intro to quant finance. I will expect students to have no quantitative background. If the course does well, I will consider building more of them. The plan is to start very basic and then slowly build to more complex ideas without leaving people behind.
@@DimitriBianco That sounds fantastic. I've been reading some of the articles on QuantStart and other basic quant resources, and it's some really interesting material. I have a background in engineering so the foundations in math and CS aren't really stumbling blocks, and there is no problem with rigor (other than the time + energy commitment required to get full coverage of the field). I, as many others, prefer self-study and have found great success in that. As you've mentioned on your videos many times, however, there is a lot of hype and ego in this field, an overwhelming amount of 'gambling addict' mentality, and it's somewhat difficult to sort between that and the legitimate art unless you either have the hands-on experience or guidance from an expert on what type of material is relevant.
I think using years as a metric for masters is not good. I would rather you say units. Some people can take lax schedule for a years worth of units in masters and make it two years.
The come up with your own fun problem Quant thesis describes my MQF program...
What about applied statistics vs statistics programs
Agreed! This needs to be higher up
Statistics degrees are great! I've mentioned in a few other videos that companies do hire quite a few stats masters and phds. As for applied vs traditional...it really depends on the program and how they define applied vs traditional.
@@DimitriBianco from what I’ve researched applied is less theory than a traditional statistics masters. Applied has more of a programming component but less theory, almost like “data science” but with more of a quantitative focus.
Traditional stat is almost no programming, more deeper in theory.
If I were to compare two people one with applied stats ms and one with pure stats ms, the applied stats is a better programmer but the pure stats is better at proofs/theory.
Dimitri, I'm having a very hard time picking a graduate school to go to. You recommended Risk Management as a career for me because I like math, stats, and finance but don't want to do software engineering, and you recommended I pursue a graduate degree in statistics. However, there are so many schools offering statistics degrees without seeming to offer financial engineering or financial mathematics courses. Filtering is hard when there are thousands of programs. How can I figure out the best program to pursue? Also, you mentioned that many would discount an unheard-of school. Does this mean I would want to go to a school that's well known specifically for statistics, a school that is well known specifically for financial engineering/mathematics, or a school that is well known in general? I feel so lost and anxious...
I'll do a video on this topic.
@@DimitriBianco Awesome, thank you so much. I'll be looking forward to it!
Please do a video about quant masters in the UK
Which program has all of these requirements? I am having trouble finding them. Please help.
To my knowledge no program exists. This information should be used to help select a program though. For example 2 year programs are very rare. However a 1.5 year program would be better than a 1 year program.
What programs would you recommend? What should one study during undergrad? What program for undergrad at nyu would best prepare me?
I can't speak directly for NYU but math and stats are good undergraduate degrees for quant finance masters preparation.
I have a list of programs however it is not exhaustive (I have limited resources to review programs).
www.fancyquantnation.com/quant-finance-list
hey dimitri love your videos. what paths are there into Quant having a UK bachelors degree in computer science from a top 25 overall school in the UK and having significant experience in coding projects and also lots of experience with machine learning and NLP projects. I'm 20 and i also have previous work experience at a startup for 1 year doing Data Science / Analytics
Any opinion on Erasmus school of economics msc in quantitative finance?
Sorry, I don't know their program well enough to give an answer.
What is your opinion on PRM? Is it any good like FRM? I have a bsc in economics and studied msc in applied statistics and I am considering to take exams for either PRM or FRM, because I want to start my career as a credit risk modeler, or even market risk modeler, but preferably credit risk.
Hey thanks for the video.
Do you think it's wise to go straight into a Master's after graduating, or should I work for a few years then do a master's?
Cheers
I would go straight for the masters. It's not very common to have work experience in between for quants.
I think there is no program with all of that!
If there is, can anyone point them out?
No such program currently exists but the video provides a guide to what a prefect program in my opinion would look like.
Can you be self taught quant?
will that be much more difficult to find quant jobs in US if my master/phd is not in US, although it is a top school with ranking
Yes, if your degree is from outside the US it will be more challenging to find work in the US. For quants I've only worked with two who had graduate degrees not from the US. They both had degrees from Canada though which is close.
I might have had one colleague with a degree from eastern Europe but they were also much older. It's been too many years for me to remember the details.
thank you!
Hello do you mind sharing your salaries in your first 3 years after your masters
Making that public feels somewhat unprofessional.
His salaries doesn’t detract from his information. He was on 6 figures after a while he mentioned tho...